Development of new analytical methods enhancing the sensitive detection and quantification of N-glycans derived from biological and clinical samples
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Glycosylation plays important roles in many biological processes and aberrant glycosylation has been linked to many human diseases. The development of new analytical methods for qualitative and quantitative glycomics study is essential to monitor glycan changes associated with disease progress. Mass spectrometry (MS) based glycomics strategies are capable of identifying and quantifying numerous glycans derived from complex biological samples. Thus, the development of reliable and sensitive analytical methods is required. The second chapter focuses on comparing the glycomic profiling of permethylated N-glycans derived from model glycoproteins and human blood serum (HBS) using MALDI-MS as well as reverse phase (RP) LC-MALDI-MS and RPLC-ESI-MS. In the case of model glycoproteins, the glycomic profiles acquired using the three methods were very comparable. However, this was not completely true in the case of glycans derived from HBS. RP-LC-ESI-MS analysis of reduced and permethylated N-glycans derived from 250 nL of HBS allowed the confident detection of 73 glycans (the structures of which were confirmed by mass accuracy and tandem MS), while 53 and 43 structures were identified in the case of RPLC-MALDI-MS and MALDI-MS analyses of the same sample, respectively. RPLC-ESI-MS analysis facilitates automated and sensitive tandem MS acquisitions. The glycan structures that were detected only in the RPLC-ESI-MS analysis were glycans existing at low abundances. This is suggesting the higher detection sensitivity of RP-LC-ESI-MS analysis, originating from both reduced competitive ionization and saturation of detectors, facilitated by the chromatographic separation. The latter also permitted the separation of several structural isomers; however, isomeric separations pertaining to linkages were not detected. The third chapter introduced a relative quantification strategy, which employing stable isotopic iodomethane for comparative glycomic profiling by LC-ESI-MS. N-glycans released from model glycoproteins and HBS were permethylated with iodomethane (“light”) and iodomethane-d1 or iodomethane-d3 (“heavy”). The reliability of this strategy was evaluated with model glycoproteins. LC-ESI-MS comparative glycomic profiling of isotopically permethylated N-glycans derived from biological samples and glycoproteins reliably defined glycan changes associated with biological conditions or glycoproteins expression. This strategy permitted the reliable quantification of glycomic changes associated with different esophageal diseases, including high-grade dysplasia, Barrett’s disease, and esophageal adenocarcinoma. The fourth chapter investigated the chromatogram behavior of permethylated N-glycans. The relationship between retention times vs. molecular weight of dextran, dextrin and model glycans and was investigated. Also, non-polar surface area (NPSA) of glycans was calculated and compared with experimental retention time. The trends of these two are similar when intermolecular interaction was included into the calculation. Moreover, the retention time is corresponding to glycan types and branch types. Then, the N-glycans analysis model, which combining the use of high mass accuracy and retention time was applied to confirm serum N-glycans. Totally, there were 70 N-glycans compositions identified with a linear fit for each subgroup. For example, R2 for complex types N-glycans were better than 0.98. The linearity allows the prediction of N-glycans structure based on their retention time. Moreover, the retention time could be further applied to distinguish structure isomers as well as linkage isomers. The fifth chapter described a new glycan sample preparation strategy using minimized sample preparation steps and optimized procedures for N-glycan profiling of mouse brain tissue sections. Tissue sections and spotted samples first undergo on-surface enzymatic digestion to release N-glycans. The released glycans are then reduced and permethylated prior to on-line purification and LC-ESI-MS analysis. The efficiency of this strategy was initially evaluated using model glycoproteins and HBS spotted on glass or Teflon slides. The new protocol permitted the detection of permethylated N-glycans derived from 10 ng RNase B. On the other hand, 66 N-glycans were identified when injecting the equivalent of permethylated glycans derived from a 0.1-L aliquot of HBS. On-tissue enzymatic digestion of nude mouse brain tissue permitted the detection of 43 N-glycans. The relative intensities of these 43 glycans were comparable to those from a C57BL/6 mouse reported by the Consortium for Functional Glycomics (CFG). However, the sample size analyzed in the protocol described here was substantially smaller than for the routine method (sub microgram vs. mg). The on-tissue N-glycan profiling method permits high sensitivity and reproducibility and can be widely applied to assess the spatial distribution of glycans associated with tissue sections, and may be correlated with immunofluorescence imaging when adjacent tissue sections are analyzed. In the sixth chapter, an LC-MS based automated data annotation and quantitation software, MultiGlycan-ESI, was utilized for glycan quantitation. Data integrated by the software were first compared with manual integration to evaluate the performance of the automatic quantitation. MultiGlycan-ESI was then applied for quantitation of different concentration of fetuin as well as fetuin spiked in a complex biological sample-HBS. The relative abundance differences between software integration and manual integration were less than 5%, indicating the reliability of this software tool in quantitation. Automated quantitation resulted in a linear relationship of R2 higher than 0.93 for all six N-glycans derived from 50 ng to 400 ng fetuin. Spiking N-glycans into 0.02 L of HBS also exhibited linear agreement between concentration and intensity. With a variety of options that include mass accuracy, merged adducts, and filtering criteria, MultiGlycan-ESI allows automated annotation and quantitation of N-glycan data acquired by LC-ESI-MS. The software facilitates rapid and reliable high-throughput glycomics studies. In the seventh chapter, a systematic comparison of N-linked profiles between MYCN-non amplified SY5Y and MYCN-amplified NLF cell lines with the aim of identifying sugar abundance changes linked to high-risk neuroblastoma was performed. Through a combination of LC-MS and bioinformatics analysis, we identified 16 glycans that show statistically significant changes in abundance between NLF and SY5Y samples. Closer examination revealed the preference for larger (in terms of total monosaccharide count) and more sialylated glycan structures in the MYCN-amplified samples relative to smaller, non-sialylated glycans that are more dominant in the MYCN non-amplified samples. These results suggest potential biomarker candidates prompting accurate neuroblastoma risk diagnosis.